Hi guys, I wanted to ask your opinion about some weird result that I get. To establish the significance I randomly permute my labels and I get a prediction rate of 0.6 and even above it (p-value=0.05). In other words 5% of of permuted samples result in 0.6+ prediction rate. The training/test samples are independent and ROI size is small (no overfitting). Interestingly, the described result I get when I average trials within block (use one data-point per block; ~25 blocks in total). When I run the classification on raw trials, my permutation threshold becomes ~0.55. In both cases for non-permuted labels the prediction is around significance level. How should I treat such a result? What might have gone wrong?
Thanks a lot for help, Vadim
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